cdec cleanup, remove bayesian stuff, parsing stuff

1 files changed, 0 insertions, 300 deletions

diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfRuleLineSearch.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfRuleLineSearch.java
deleted file mode 100644
index 5489f2d0..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfRuleLineSearch.java
+++ /dev/null
@@ -1,300 +0,0 @@
-package optimization.linesearch;
-
-import java.io.PrintStream;
-import java.util.ArrayList;
-
-import optimization.util.Interpolation;
-
-
-
-
-/**
- * 
- * @author javg
- *
- */
-public class WolfRuleLineSearch implements LineSearchMethod{
-
-	GenericPickFirstStep pickFirstStep;
-	
-	double c1 = 1.0E-4;
-	double c2 = 0.9;
-	
-	//Application dependent
-	double maxStep=100;
-	
-	int extrapolationIteration;
-	int maxExtrapolationIteration = 1000;
-	
-	
-	double minZoomDiffTresh = 10E-10;
-
-	
-	ArrayList<Double> steps;
-	ArrayList<Double> gradientDots;
-	ArrayList<Double> functionVals;
-	
-	int debugLevel = 0;
-	boolean foudStep = false;
-	
-	public WolfRuleLineSearch(GenericPickFirstStep pickFirstStep){
-		this.pickFirstStep = pickFirstStep;
-		
-	}
-	
-	
-
-	
-	public WolfRuleLineSearch(GenericPickFirstStep pickFirstStep,  double c1, double c2){
-		this.pickFirstStep = pickFirstStep;
-		initialStep = pickFirstStep.getFirstStep(this);
-		this.c1 = c1;
-		this.c2 = c2;
-	}
-	
-	public void setDebugLevel(int level){
-		debugLevel = level;
-	}
-	
-	//Experiment
-	double previousStepPicked = -1;;
-	double previousInitGradientDot = -1;
-	double currentInitGradientDot = -1;
-	
-	double initialStep;
-
-	
-	public void reset(){
-		previousStepPicked = -1;;
-		previousInitGradientDot = -1;
-		currentInitGradientDot = -1;
-		if(steps != null)
-			steps.clear();
-		if(gradientDots != null)
-			gradientDots.clear();
-		if(functionVals != null)
-			functionVals.clear();
-	}
-	
-	public void setInitialStep(double initial){
-		initialStep = pickFirstStep.getFirstStep(this);
-	}
-	
-	
-	
-	/**
-	 * Implements Wolf Line search as described in nocetal.
-	 * This process consists in two stages. The first stage we try to satisfy the
-	 * biggest step size that still satisfies the curvature condition. We keep increasing
-	 * the initial step size until we find a step satisfying the curvature condition, we return 
-	 * success, we failed the sufficient increase so we cannot increase more and we can call zoom with 
-	 * that maximum step, or we pass the minimum in which case we can call zoom the same way. 
-	 * 
-	 */
-	public double getStepSize(DifferentiableLineSearchObjective objective){
-		//System.out.println("entering line search");
-		
-		foudStep = false;
-		if(debugLevel >= 1){
-			steps = new ArrayList<Double>();
-			gradientDots = new ArrayList<Double>();
-			functionVals  =new ArrayList<Double>();
-		}
-		
-		//test
-		currentInitGradientDot = objective.getInitialGradient();
-		
-		
-		double previousValue = objective.getCurrentValue();
-		double previousStep = 0;
-		double currentStep =pickFirstStep.getFirstStep(this);
-		for(extrapolationIteration = 0; 
-		extrapolationIteration < maxExtrapolationIteration; extrapolationIteration++){	
-			
-			objective.updateAlpha(currentStep);
-			double currentValue = objective.getCurrentValue();
-			if(debugLevel >= 1){
-				steps.add(currentStep);
-				functionVals.add(currentValue);
-				gradientDots.add(objective.getCurrentGradient());
-			}
-			
-			
-			//The current step does not satisfy the sufficient decrease condition anymore
-			// so we cannot get bigger than that calling zoom.
-			if(!WolfeConditions.suficientDecrease(objective,c1)||					
-					(extrapolationIteration > 0 && currentValue >= previousValue)){
-				currentStep = zoom(objective,previousStep,currentStep,objective.nrIterations-1,objective.nrIterations);
-				break;
-			}
-			
-			//Satisfying both conditions ready to leave
-			if(WolfeConditions.sufficientCurvature(objective,c1,c2)){
-				//Found step
-				foudStep = true;
-				break;
-			}
-			
-			/**
-			 * This means that we passed the minimum already since the dot product that should be 
-			 * negative (descent direction) is now positive. So we cannot increase more. On the other hand
-			 * since we know the direction is a descent direction the value the objective at the current step
-			 * is for sure smaller than the preivous step so we change the order.
-			 */
-			if(objective.getCurrentGradient() >= 0){
-				currentStep =  zoom(objective,currentStep,previousStep,objective.nrIterations,objective.nrIterations-1);
-				break;
-			}
-			
-			
-			//Ok, so we can still get a bigger step, 
-			double aux = currentStep;
-			//currentStep = currentStep*2;
-			if(Math.abs(currentStep-maxStep)>1.1e-2){
-				currentStep = (currentStep+maxStep)/2;
-			}else{
-				currentStep = currentStep*2;
-			}
-			previousStep = aux;
-			previousValue = currentValue;
-			//Could be done better
-			if(currentStep >= maxStep){
-				System.out.println("Excedded max step...calling zoom with maxStepSize");
-				currentStep = zoom(objective,previousStep,currentStep,objective.nrIterations-1,objective.nrIterations);
-			}
-		}
-		if(!foudStep){
-			System.out.println("Wolfe Rule exceed number of iterations");
-			if(debugLevel >= 1){
-				printSmallWolfeStats(System.out);
-//				System.out.println("Line search values");
-//				DebugHelpers.getLineSearchGraph(o,  direction, originalParameters,origValue, origGradDirectionDot,c1,c2);			
-			}
-			return -1;
-		}
-		if(debugLevel >= 1){
-			printSmallWolfeStats(System.out);
-		}
-
-		previousStepPicked = currentStep;
-		previousInitGradientDot = currentInitGradientDot;
-//		objective.printLineSearchSteps();
-		return currentStep;
-	}
-	
-	
-	
-	
-	
-	public void printWolfeStats(PrintStream out){
-		for(int i = 0; i < steps.size(); i++){		
-			out.println("Step " + steps.get(i) + " value " + functionVals.get(i) + " dot " + gradientDots.get(i));
-		}
-	}
-	
-	public void printSmallWolfeStats(PrintStream out){
-		for(int i = 0; i < steps.size(); i++){		
-			out.print(steps.get(i) + ":"+functionVals.get(i)+":"+gradientDots.get(i)+" ");
-		}
-		System.out.println();
-	}
-	
-	
-	
-	/**
-	 * Pick a step satisfying the strong wolfe condition from an given from lowerStep and higherStep
-	 * picked on the routine above.
-	 * 
-	 * Both lowerStep and higherStep have been evaluated, so we only need to pass the iteration where they have
-	 * been evaluated and save extra evaluations.
-	 * 
-	 * We know that lowerStepValue as to be smaller than higherStepValue, and that a point 
-	 * satisfying both conditions exists in such interval.
-	 * 
-	 * LowerStep always satisfies at least the sufficient decrease
-	 * @return
-	 */
-	public double zoom(DifferentiableLineSearchObjective o, double lowerStep, double higherStep,
-			int lowerStepIter, int higherStepIter){
-		
-		if(debugLevel >=2){
-			System.out.println("Entering zoom with " + lowerStep+"-"+higherStep);
-		}
-		
-		double currentStep=-1;
-		
-		int zoomIter = 0;
-		while(zoomIter < 1000){		
-			if(Math.abs(lowerStep-higherStep) < minZoomDiffTresh){
-				o.updateAlpha(lowerStep);
-				if(debugLevel >= 1){
-					steps.add(lowerStep);
-					functionVals.add(o.getCurrentValue());
-					gradientDots.add(o.getCurrentGradient());
-				}
-				foudStep = true;
-				return lowerStep;
-			}	
-	
-			//Cubic interpolation
-			currentStep = 
-				Interpolation.cubicInterpolation(lowerStep, o.getValue(lowerStepIter), o.getGradient(lowerStepIter), 
-						higherStep, o.getValue(higherStepIter), o.getGradient(higherStepIter));
-			
-			//Safeguard.... should not be required check in what condtions it is required
-			if(currentStep < 0 ){
-				currentStep = (lowerStep+higherStep)/2;
-			}
-			if(Double.isNaN(currentStep) || Double.isInfinite(currentStep)){
-				currentStep = (lowerStep+higherStep)/2;
-			}
-//			currentStep = (lowerStep+higherStep)/2;
-//			System.out.println("Trying "+currentStep);
-			o.updateAlpha(currentStep);
-			if(debugLevel >=1){
-				steps.add(currentStep);
-				functionVals.add(o.getCurrentValue());
-				gradientDots.add(o.getCurrentGradient());
-			}
-			if(!WolfeConditions.suficientDecrease(o,c1)
-					|| o.getCurrentValue() >= o.getValue(lowerStepIter)){
-				higherStepIter = o.nrIterations;
-				higherStep = currentStep;
-			}
-			//Note when entering here the new step satisfies the sufficent decrease and
-			// or as a function value that is better than the previous best (lowerStepFunctionValues)
-			// so we either leave or change the value of the alpha low.
-			else{
-				if(WolfeConditions.sufficientCurvature(o,c1,c2)){
-					//Satisfies the both wolf conditions
-					foudStep = true;
-					break;
-				}
-				//If does not satisfy curvature 
-				if(o.getCurrentGradient()*(higherStep-lowerStep) >= 0){
-					higherStep = lowerStep;
-					higherStepIter = lowerStepIter;
-				}
-				lowerStep = currentStep;
-				lowerStepIter = o.nrIterations;
-			}
-			zoomIter++;
-		}
-		return currentStep;
-	}
-
-	public double getInitialGradient() {
-		return currentInitGradientDot;
-		
-	}
-
-	public double getPreviousInitialGradient() {
-		return previousInitGradientDot;
-	}
-
-	public double getPreviousStepUsed() {
-		return previousStepPicked;
-	}
-	
-	
-}